High-frequency control problems are often studied through time discretization, but refining the timestep $δ$ increases the discrete horizon $H=T/δ$ and can make standard finite-horizon regret guarantees deteriorate even when the physical horizon $T$ is fixed. We study episodic value-based reinforcement learning for Euler discretizations of controlled diffusions. The performance is measured as the cumulative regret over $K$ episodes relative to the optimal policy of the corresponding Euler model. Under stability and regularity conditions on value function gradients, Bellman residual derivatives, and fitted critics, we prove an $\mathcal{O}(\log K)$ regret bound with constants uniform in $δ$. For dynamics with drift linear in unknown parameters and unknown constant diffusion covariance, we verify localized versions of these conditions for a constrained, model-based controlled-diffusion fitted-$Q$ algorithm (CD-FQ) and obtain high-probability $\mathcal{O}(\log K\log\log K)$ regret for fixed and finite $T$, again with constants uniform in $δ$.
Mingyuan Xu, Zongqi Xia, Tianxi Cai, Doudou Zhou, Nian Si
Learning a common policy from multiple source environments requires accounting for cross-source heterogeneity, and the offline setting introduces an additional challenge of limited data coverage. We formulate this problem as a distributionally robust Markov decision process under a shared linear MDP representation with source-specific transition and reward components. The uncertainty set is constructed through feature-wise mixtures of these components, yielding a $d$-rectangular structure that captures cross-source heterogeneity and admits a robust Bellman recursion. Building on this formulation, we develop Group-Robust Pessimistic Value Iteration (GR-PEVI), an offline policy-learning algorithm that combines feature-wise worst-case aggregation with a data-dependent pessimism penalty. Under a robust partial coverage condition, we establish finite-sample suboptimality guarantees for GR-PEVI and extend the analysis to clustered sources, characterizing the trade-off between the statistical gain from pooling similar sources and the approximation error induced by within-cluster heterogeneity. Simulation studies demonstrate the effectiveness of GR-PEVI, with empirical behavior consistent with the theoretical analysis. We apply GR-PEVI to a multi-source electronic health record study of multiple sclerosis treatment decisions to illustrate its use in a clinical setting.
Yixue Zhang, Kun Wu, Zhi Gao, Zhen Zhao, Pei Ren, Zhiyuan Xu, Fei Liao, Xinhua Wang 等 15 位作者
The pursuit of general-purpose robotic manipulation is hindered by the scarcity of diverse, real-world interaction data. Unlike large-scale web vision-language data, robotic data collection incurs prohibitive physical costs. The key challenge is thus not merely to collect more robot data, but to obtain informative training data that broadens behavioral coverage across objects, scenes, and skills. Yet automatically curating task proposals to maximize the utility of collected data remains under-explored. We introduce RoboGene, an agentic framework for generating diverse, informative manipulation tasks across single-arm, dual-arm, and mobile robots. RoboGene combines (1) diversity-driven sampling for broad task coverage, (2) agentic self-reflection for task-quality assessment, and (3) human-verified feedback memory for execution-grounded validation and continual refinement. Through quantitative analysis and large-scale real-world experiments, we collect 18k trajectories and introduce metrics for task feasibility and diversity. RoboGene significantly outperforms frontier foundation models, including GPT-4o and Gemini 2.5 Pro. VLA models trained on RoboGene-generated data achieve higher success rates and stronger generalization, highlighting the value of high-quality task generation. Project page: https://robogene-boost-vla.github.io/. Datasets: Hugging Face (https://huggingface.co/datasets/X-Humanoid/RoboGene) and ModelScope (https://modelscope.cn/datasets/X-Humanoid/RoboGene_tasks).